The amount of data being processed and stored is rapidly increasing as technological advances allow users to generate and store increasing amounts of data. Today, large sets of data can be stored in various data structures such as databases. For example, information associated with finger prints and facial recognition systems are stored in large datasets. Similarly, information associated with hospital records, financial records, and legal documents are also stored in large data structures. Moreover, information associated with merchant transactions such as payment card information can be stored.
As data storage became more affordable, large and complex datasets became more ubiquitous. Advances in computing technology similarly helped fuel the growth of what is commonly referred to as Big Data. In addition to the rise of Big Data, during the same period payment card transactions surpassed over 50% of non-cash transactions, as personal checks grew out of favor. Part of this was due to the rising popularity of debit cards which, as opposed to credit cards, allowed money to be transferred directly from a user's account rather than requiring a user to pay a credit card company the money at a later date.
Data breaches involving payment card information has also increased in recent decades. Large data structures used to store payment card information became increasingly popular as merchants were able to monitor user behavior based on payment card information and transaction information involving those payment cards. The sheer amount of information included in these data structures, combined with outdated technology, in some cases, has fueled an increase in payment card breaches. These breaches, whether caused by a hacked card reader, or a hacked data structure, can potentially put information associated with thousands of payment cards into the hands of unauthorized users.
Breaches perpetrated by bad actors such as hackers are increasingly sophisticated. When gaining access to information, these hackers use a variety of techniques to disguise their activities. For instance, a hacker may gain access to a card reader or data structure, and wait for a period of time before using stolen card data. As such, companies that are attacked may not know about the attack for weeks or even months. Further, when an issuing bank or card association discovers a breach, the bank or association may not be able to easily trace the source of a breach. They may notice that many cards are being reported as compromised, but not have a way to determine the date or location of where the card information was stolen. This, in turn, exposes a company, bank, or association to further financial liability because there may be additional compromised cards that have yet to be identified.
Thus, there is a need in the art for a better way to determine the date and location of potential breaches. By determining when and where a breach occurred, a company, an issuing bank, or a card association may be able to identify potentially compromised cards and notify the cards' holders or deactivate the cards. This determination, however, can be difficult because the amount of data required is very large. Previously, many cards would need to be reported as compromised before a company, bank, or association could determine any information related to a breach, and a bank or financial institution would have to piece together circumstantial evidence of a potential breach by cross-referencing transaction data. This process was time consuming and often did not reliably indicate when or where a breach had occurred. As such, because data associated with millions of card transactions does not avail itself to trend determination with ease, new systems are currently being developed to identify breaches in very little time.